A New Approach Based on Image Processing for Measuring Compressive Strength of Structures

  • Mehmet Baygin
  • Suat Gokhan Ozkaya Ardahan University
  • Muhammed Alperen Ozdemir
  • Ilker Kazaz
Keywords: Artificial Neural Networ, Compressive Strength, Image Processing

Abstract

The compressive strength factor in civil engineering is a very important parameter used to determine the performance of structures. The stability of structures can be tested with this parameter which is used to measure the performance of concrete under different loads. This parameter, which should be determined for the safety of the structures, is usually based on experimental analyses performed in the laboratory environment. In this study, a new approach to compressive strength measurement in civil engineering is proposed. With this approach, which is based on image processing, measurement of compressive strength parameter of concrete samples taken from structures is performed. For this purpose, images of concrete specimens with different strengths are taken and these images are divided into two groups as training and test set. Then, image processing algorithms are applied to these images and the compressive strength of concrete specimens is calculated. It has been determined that the approach suggested in the test runs performed with an error rate of about 1-2%

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References

H. Eskandari-Naddaf, and T. Kazemi, “ANN prediction of cement mortar compressive strength, influence of cement strength class,” Construction and Building Materials, vol. 138, pp. 1-11, 2017.

E. Quagliarini, F. Clementi, G. Maracchini, and F. Monni, “Experimental assessment of concrete compressive strength in old existing RC buildings: A possible way to reduce the dispersion of DT results,” Journal of Building Engineering, vol. 8, pp. 162-171, 2016.

G. Tiberti, F. Minelli, and G. Plizzari, “Cracking behavior in reinforced concrete members with steel fibers: a comprehensive experimental study,” Cement and Concrete Research, vol. 68, pp. 24-34, 2015.

E. Arioglu, and M. N. Arioglu, “Concrete Core Experiments and Evaluation in Top and Bottom Structures,” Evrim Publisher, 2005.

P. Soroushian, M. Elzafraney, and A. Nossoni, “Specimen Preparation and Image Processing and Analysis Techniques for Automated Quantıification of Concrete Microcracks and Voids,” Cement and Concrete Research, vol. 33, pp. 1949–1962, Nov. 2003.

M. K. Head, and N. R. Buenfeld, “Measurement of Aggregate Interfacial Porosity in Complex, Multi-Phase Aggregate Concrete: Binary Mask Production Using Backscattered Electron, and Energy Dispersive X-Ray Images,” Cement and Concrete Research, vol. 36, pp. 337–345, 2006.

E. K. K. Nambiar, and K. Ramamurthy, “Air‐void Characterisation of Foam concrete,” Cement and Concrete Research, vol. 37, pp. 221–230, 2007.

M. Lopez, L. F. Kahn, and K. E. Kurtis, “Characterization of Elastic and Time-Dependent Deformations in High Performance Lightweight Concrete by Image Analysis,” Cement and Concrete Research, vol. 39, pp. 610–619, 2009.

M. Baygin, and M. Karakose, “A new image stitching approach for resolution enhancement in camera arrays,” 9th International Conference on In Electrical and Electronics Engineering (ELECO), 2015, pp. 1186-1190.

M. Karakose, and M. Baygin, “Image processing based analysis of moving shadow effects for reconfiguration in pv arrays,” IEEE International in Energy Conference (ENERGYCON), 2014, pp. 683-687.

S. N. Yu, J. H. Jang, and C. S. Han, “Auto Inspection System Using a Mobile Robot for Detecting Concrete Cracks in a Tunnel,” Automation in Construction, vol. 16, p. 255–261, 2007.

M. E. Oncu, A. S. Karakas, and M. T. Kavak, “Production of High Performance Concrete Using Admixtures,” Journal of Engineering and Natural Sciences, vol. 2, pp. 76–82, 2006.

G. Cankaya, M. H. Arslan and M. Ceylan, “Determination of Pressure Strength of Concrete with Image Processing and Artificial Neural Networks,” Selcuk Univ. J. Eng. Sci. Tech., vol. 1, no. 1, 2013.

C. Basyigit, S. Kilincarslan, and B. Comak, “Prediction of Concrete Pressure Strength by Image Processing Technique,” Suleyman Demirel University, Graduate School of Natural and Applied Sciences Journal, vol. 16, 82–88, 2012.

A. Ergun, and G. Kurklu, “Determination of Concrete Resistance in Existing Reinforced Concrete Structures,” Earthquake Symposium, 2005, pp. 817-826.

Published
2017-07-31
How to Cite
[1]
M. Baygin, S. Ozkaya, M. Ozdemir, and I. Kazaz, “A New Approach Based on Image Processing for Measuring Compressive Strength of Structures”, IJISAE, pp. 21-25, Jul. 2017.
Section
Research Article